import argparse, uuid, toml

from utils.datasets import *
from utils.utils import *
import pandas as pd 


def save_box_info(x, img, config, toml_name, img_name, cur_frame, df, color=None, label=None, line_thickness=None):

    toml_path_images = 'automl/images'
    toml_path_annt = 'automl/annotations'

    # Plots one bounding box on image img
    tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
    color = color or [random.randint(0, 255) for _ in range(3)]
    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
    df.loc[len(df)] = [c1[0],c1[1],c2[0],c2[1],label,cur_frame]
    if not (os.path.isfile('dets.csv')):
        df.to_csv('dets.csv',index=False)
    else:
        df.to_csv('dets.csv',mode = 'a+', index=False, header=False)
    # df = pd.DataFrame(columns = ['tlx','tly','brx','bry','cls','frame'])
    # print (df.shape)
    # cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
    return df



def plot_one_box_(x, img, config, toml_name, img_name, color=None, label=None, line_thickness=None):

    toml_path_images = 'automl/images'
    toml_path_annt = 'automl/annotations'
    cls_names = ['person','car','traffic_light','stop_sign']
    cls_names_ori = ['person','car','traffic light','stop sign']

    # Plots one bounding box on image img
    tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
    color = color or [random.randint(0, 255) for _ in range(3)]
    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
    # imc = img.copy()
    # cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
    if label == 'truck':
        label = 'car'

    if label and (label in cls_names_ori):
        lidx = cls_names_ori.index(label)
        config['objects'] = [{'xmin': c1[0], 'ymin': c1[1],'ymax': c2[1], 'xmax': c2[0],'class': cls_names[lidx]}]
        tf = max(tl - 1, 1)  # font thickness
        t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
        c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
        # print (label, c1, c2)
        
        out_path = os.path.join(toml_path_annt, toml_name)
        cv2.imwrite(os.path.join(toml_path_images,img_name),img)
        p = open(out_path, 'a+')
        p.write(toml.dumps(config))
        # cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA)  # filled
        # cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)


def detect(save_img=False):
    out, source, weights, half, view_img, save_txt, imgsz = \
        opt.output, opt.source, opt.weights, opt.half, opt.view_img, opt.save_txt, opt.img_size
    webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt')

    # Initialize
    device = torch_utils.select_device(opt.device)
    if os.path.exists(out):
        shutil.rmtree(out)  # delete output folder
    os.makedirs(out)  # make new output folder

    # Load model
    google_utils.attempt_download(weights)
    model = torch.load(weights, map_location=device)['model']
    # torch.save(torch.load(weights, map_location=device), weights)  # update model if SourceChangeWarning
    # model.fuse()
    model.to(device).eval()

    # Second-stage classifier
    classify = False
    if classify:
        modelc = torch_utils.load_classifier(name='resnet101', n=2)  # initialize
        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model'])  # load weights
        modelc.to(device).eval()

    # Half precision
    half = half and device.type != 'cpu'  # half precision only supported on CUDA
    if half:
        model.half()

    # Set Dataloader
    vid_path, vid_writer = None, None
    if webcam:
        view_img = True
        torch.backends.cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz)
    else:
        save_img = True
        dataset = LoadImages(source, img_size=imgsz)

    # Get names and colors
    names = model.names if hasattr(model, 'names') else model.modules.names
    colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]

    # Run inference
    t0 = time.time()
    img = torch.zeros((1, 3, imgsz, imgsz), device=device)  # init img
    _ = model(img.half() if half else img.float()) if device.type != 'cpu' else None  # run once
    cnt=0
    df = pd.DataFrame(columns = ['tlx','tly','brx','bry','cls','frame'])
    for path, img, im0s, vid_cap in dataset:
        cur_frame = int(vid_cap.get(cv2.CAP_PROP_POS_FRAMES))
        cnt = cur_frame
        img = torch.from_numpy(img).to(device)
        img = img.half() if half else img.float()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        # Inference
        t1 = torch_utils.time_synchronized()
        pred = model(img, augment=opt.augment)[0]
        t2 = torch_utils.time_synchronized()

        # to float
        if half:
            pred = pred.float()

        # Apply NMS
        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres,
                                   fast=True, classes=opt.classes, agnostic=opt.agnostic_nms)

        # Apply Classifier
        if classify:
            pred = apply_classifier(pred, modelc, img, im0s)

        # Process detections
        config = dict()
        # print (im0s)
        # config['width'] = im0s[0].shape[1]
        # config['height'] = im0s[0].shape[0]
        config['width'] = im0s.shape[1]
        config['height'] = im0s.shape[0]

        
        gen_name = 'br-cam-008.streams_' + str(uuid.uuid4()) +'_' +str(cnt).zfill(5)
        img_name = gen_name + '.jpg'
        toml_name = gen_name + '.toml'
        # print (gen_name,img_name,toml_name)
        for i, det in enumerate(pred):  # detections per image
            if webcam:  # batch_size >= 1
                p, s, im0 = path[i], '%g: ' % i, im0s[i].copy()
            else:
                p, s, im0 = path, '', im0s

            save_path = str(Path(out) / Path(p).name)
            s += '%gx%g ' % img.shape[2:]  # print string
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  #  normalization gain whwh
            if det is not None and len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

                # Print results
                all_cls = []
                for c in det[:, -1].unique():
                    n = (det[:, -1] == c).sum()  # detections per class
                    s += '%g %ss, ' % (n, names[int(c)])  # add to string
                    all_cls.append(names[int(c)])
                # print (all_cls)
                # Write results
                # print (det[1])
                for *xyxy, conf, cls in det:
                    # print (cls)
                    if save_txt:  # Write to file
                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                        with open(save_path[:save_path.rfind('.')] + '.txt', 'a') as file:
                            file.write(('%g ' * 5 + '\n') % (cls, *xywh))  # label format

                    if save_img or view_img:  # Add bbox to image
                        # label = '%s %.2f' % (names[int(cls)], conf)
                        label = names[int(cls)]
                        df = save_box_info(xyxy, im0, config, toml_name, img_name,cur_frame,
                        df, label=label, 
                        color=colors[int(cls)],
                        line_thickness=3)
                        df = pd.DataFrame(columns = ['tlx','tly','brx','bry','cls','frame'])
                        # print (all_cls)
                        # if 'traffic light' in all_cls:
                        if ('stop sign' in all_cls) or ('traffic light' in all_cls):
                            plot_one_box_(xyxy, im0, config, toml_name, img_name, label=label, color=colors[int(cls)], 
                            line_thickness=3)

            # Print time (inference + NMS)
            # print('%sDone. (%.3fs)' % (s, t2 - t1))

            # Stream results
            if view_img:
                cv2.imshow(p, im0)
                if cv2.waitKey(1) == ord('q'):  # q to quit
                    raise StopIteration

            # Save results (image with detections)
            if save_img:
                if dataset.mode == 'images':
                    cv2.imwrite(save_path, im0)
                else:
                    if vid_path != save_path:  # new video
                        vid_path = save_path
                        if isinstance(vid_writer, cv2.VideoWriter):
                            vid_writer.release()  # release previous video writer

                        fps = vid_cap.get(cv2.CAP_PROP_FPS)
                        w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                        h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                        vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*opt.fourcc), fps, (w, h))
                    vid_writer.write(im0)

    if save_txt or save_img:
        # print('Results saved to %s' % os.getcwd() + os.sep + out)
        if platform == 'darwin':  # MacOS
            os.system('open ' + save_path)

    # print('Done. (%.3fs)' % (time.time() - t0))


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', type=str, default='weights/yolov5s.pt', help='model.pt path')
    parser.add_argument('--source', type=str, default='inference/images', help='source')  # file/folder, 0 for webcam
    parser.add_argument('--output', type=str, default='inference/output', help='output folder')  # output folder
    parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
    parser.add_argument('--conf-thres', type=float, default=0.4, help='object confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS')
    parser.add_argument('--fourcc', type=str, default='mp4v', help='output video codec (verify ffmpeg support)')
    parser.add_argument('--half', action='store_true', help='half precision FP16 inference')
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--view-img', action='store_true', help='display results')
    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
    parser.add_argument('--classes', nargs='+', type=int, help='filter by class')
    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
    parser.add_argument('--augment', action='store_true', help='augmented inference')
    opt = parser.parse_args()
    # print(opt)

    with torch.no_grad():
        detect()
